arXiv:2606. 29705v1 Announce Type: new Abstract: Data, as the fundamental substrate of modern intelligence, has greatly driven the development of current foundation models.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2511. 07332v2 Announce Type: replace-cross Abstract: Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen elements.
arXiv:2607. 04425v2 Announce Type: replace-cross Abstract: Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform interaction.
GUI agents require high-quality interaction trajectories to learn how software environments respond to actions, maintain state, and support multi-step workflows. However, the diversity of available tr...
arXiv:2605. 25160v2 Announce Type: replace Abstract: GUI agents powered by large language models are advancing rapidly, creating urgent needs for evaluation and training based on realistic environments.
arXiv:2610.01215v1 Announce Type: new Abstract: GUI agents require high-quality interaction trajectories to learn how software environments respond to actions, maintain state, and support multi-step...
EviRover is a perception agent that goes beyond a single glance by actively gathering information to resolve perceptual queries. The authors created two data generation pipelines, producing EviRover-SFT-5K and EviRover-RL-12K, and a human‑verified benchmark called EviLens with 688 instances across five perception categories. Trained with supervised fine‑tuning and agentic reinforcement learning, the 4B EviRover outperforms its backbone by an average of 30 points on EviLens and shows strong transfer to other benchmarks such as WebEyes and BrowseComp‑VL.